The Effects of Venture Capital Strategy


Highland Capital and Tallwood Venture Capital represent two strategies of venture capital investing. Highland represents the traditional diversified portfolio strategy where many unrelated investments are made and little operational support is provided by the investor. Tallwood represents the focused portfolio strategy where fewer investments are made in a specific industry and investors are able to provide a greater amount of time and operational support to their firms. Neither of these firms is absolute in their strategic focus. Highland provides some support to some of its investments in the form of office space and access to other industry leaders. Highland also has focused teams in specific industries and cannot simply invest in any firm. While I will discuss a hybrid approach later in the paper, the number of focused sectors and number of investments in each sector results in a strategy that is indistinguishable from a diversified strategy.

Tallwood only has two executives in residence to assist partners in providing technical advise for its portfolio. Clearly, this is insufficient to provide constant support to their firms however this paper will discuss relative differences in strategies rather than absolute examples of either strategy. I will explain why this difference matters to investors, how it affects investment selection within the firm, and the type of firms that are most likely to receive funding from each strategy. Given the extremely high exits necessary to compensate for a large number of losers, a diversified strategy is interested in funding companies that have a low probability of success and a high-expected growth rate. A high probability of success would not allow for a large enough equity stake given a limited amount of equity and a low growth rate does not provide a sufficiently high exit valuation. Increasing an investor’s focus on an industry allows for investing in ideas that may have a smaller market potential but greater probability of success.

Investor Motivation

From the investor’s point of view, venture capital should provide superior risk adjusted returns with a liquidity premium when compared with public markets. Without this return, there is no incentive to invest. The problem lies with describing risk in statistical terms in order to get a precise quantitative answer rather than business terms that usually results in a more qualitative answer.

Investing in a venture capital firm requires a lockup period where the investor cannot withdraw the investment or may withdraw but at a severe penalty. This is to protect other investors in the fund from being forced to contribute more capital than planned or to sell illiquid investments at a discount. As compensation for being unable to quickly withdraw their investment, an investor requires a liquidity premium. Without this liquidity premium, investors would be better off buying assets such as stocks with the same returns and no penalty for selling early. Endowments that have too much of their portfolios allocated to illiquid assets may not be able to support their liquidity needs when a financial crisis causes the value of their stocks and bonds to fall. When the liquidity risk is not considered, venture capital returns may be overstated.

While venture capital may be an unappealing investment based on overestimated returns and underestimated risk, there may be opportunities to make a rational investment by choosing a successful fund manager. If an investor will need to accept a lockup period of up to 10 years, the initial manager selection is critical. There is a substantial risk that one of the partners may retire or leave the firm and support staff typically has a higher turnover so the selection process will need to focus on strategy rather than personality. Knowing this, the question then becomes what strategy produces above average returns in the venture capital industry?

Venture Capital Industry Concerns 

According to Fred Wilson, venture firms need average exit multiples of three to meet the returns required by investors. The average industry exit multiple was 1.6 which is approximately a ten percent annual IRR. According to the article, another unnamed industry analyst estimated that venture capital would need to decrease by 50% in order to generate adequate risk adjusted returns before coming to the conclusion that the venture capital model doesn’t scale. This conclusion seems justified when we compare the expectation of a 10% return in 2010 with the expectation of a 25% to 35% return in 1998 and the realized return of 8.2% as of the third quarter of 2010 but does not take into account a strategy change in the venture capital industry.

Highland Capital represents the traditional diversified venture capital strategy. As Peter Bell noted in his presentation, the success of any investment is viewed as a random process and Highland’s partners are generalists that follow a top down approach to individual investments. The partner first determines an attractive industry; one that is in the growth period of the industry S-curve. High growth companies command higher valuations at exit and are easier to market to the public market or an acquirer. Since there is little disagreement in the industry on what the high growth industries will be, venture firms will tend to over invest in attractive industries.

According to Zider, a venture capital investment has an estimated 10% probability of success, which implies that the average venture capital firm has excess capital to allocate to investments. The exact proportion of the remaining 90% that is required to obtain the winning 10% cannot be determined with certainty without making some assumptions. In Highland Capital’s case, the assumption is that venture investors are not skilled in selecting investments. This assumption supports the diversification strategy also limiting the amount of time each partner can devote to an individual investment. A generalist who invests in a diversified portfolio has little incentive to choose the best firm in an attractive industry with time being a limited resource. This conclusion is based on the assumption of limited equity in quality firms seeking capital and limited time combined with excess funds with which to invest.

In Tallwood’s case, the firm has a focused portfolio strategy where the top down approach can only be applied in a limited manner. If investments are limited to the semiconductor industry and are smaller in number, Tallwood can pursue a bottom up investment strategy where more time is spent evaluating whether the particular investment is attractive as opposed to evaluating whether an industry is attractive. Time constraints become more relaxed relative to a diversified strategy since there are fewer investments to track. The focused portfolio strategy assumes that partners have some expertise in selecting investments and advising companies. Without this assumption, there would be no incentive to limit the number of investments or the industry in which to invest.

An alternative to the diversified and focused portfolio strategies is for a single venture capital firm to have multiple partners where each partner or team of partners is focused on a particular industry or technology. This hybrid strategy can mimic the results of either the diversified or focused portfolio depending on the number of investments made by each partner and the resources available for each partner or team to support their investments. A partner with few investments has an incentive to select best in class companies and provide operational support while a partner with many investments will provide little to no support and assume success is a random process. In the former example, the hybrid strategy will mimic a focused strategy while in the latter case; the strategy will be similar to a diversified strategy.

Financial Theory

The assumptions I have made regarding diversified and focused portfolio strategies in the venture capital industry are supported by the work of Fisher Black and Robert Litterman in their model of portfolio optimization. The Black Litterman model was original applied to currencies but it can be applied to any investment. The main idea of this model is to optimize a portfolio not just on expected return but also on the deviation between market expected returns and the investor’s expected return. In essence, invest more money when the investor believes that he/she has an edge over the market.

The diversified strategy is diversified because partners believe that they do not have more skill at selecting investments than the market. Under the Black Litterman model, we would expect these firms to exhibit “herding” behavior investing in the same firms and industries and, as Peter Bell confirmed, this is what we observe in the industry. Focused funds believe that they have an edge over other investors and overweight those firms as described by the model. This results in fewer investments and more resources to support each investment.

Returning to the argument of the amount of excess capital necessary to secure a 10% success rate, the focused fund must assume that either they have a higher success rate than diversified funds or they have higher returns from their winners. Without one or both of these assumptions, there would be no incentive to remain limited to a focused strategy. If we assume that one or both of these possibilities are true, a focused firm has the opportunity to invest in firms where the expected return is lower than that required of a diversified fund. This occurs because of the secondary weighting of the difference between investor expectations and market expectations described by the Black Litterman model. The two components of expected return, possible returns weighted by probabilities of success, have some interesting implications regarding the type of investments that each strategy will prefer.

Impact on Funded Companies

In the case of Highland, a diversified portfolio results in many companies receiving funding with each project receiving little to no operational guidance. Given the extremely high exits necessary to compensate for a large number of losers, the type of ideas that get funding are those that have a low probability of success, a high growth rate, and a high expected return. Knowing that there is excess capital when measured against the limited amount of quality equity for the reasons discussed above, only a low probability of success will guarantee a sufficient equity stake to compensate for the large number of losers in the diversified portfolio. A high probability of success would not allow for a large enough equity stake and would not provide sufficient returns for the diversified investor.

Increasing an investor’s focus on an industry allows for investing in ideas that may have a smaller market potential but greater probability of success. This type of investing serves several important purposes. First, it is obviously an efficient allocation of capital that produces economic growth. Second, it provides a form of financing that is less expensive than the most speculative angel investing but more expensive than traditional debt financing. Usury laws prohibit charging an appropriate interest rate for the amount of risk that investors are exposed to even when investing in a company with a greater probability of success. This is because many firms that fit this description may not have sufficient physical assets to post as collateral. Firms with a focused strategy ensure that companies that may not change the world but may create substantial economic value receive funding.

Recent data from the National Venture Capital Association suggests that investors are on average pursuing a diversified strategy. Despite low and even negative returns the number of deals remains high while the amount invested decreases. This would suggest that venture capital investors are making numerous small investments rather than a small number of large investments. The most recent data also suggest that investors continue to choose the same sectors in which to invest. Cambridge Associates reports that 75% of its venture capital benchmark is composed of healthcare, IT, and software and that these sectors outperform other venture capital investments. This would seem to support the idea that most investors do not deviate from market expectations and under the Black Litterman model, would be best served by pursuing the diversified portfolio strategy.

If most venture firms are investing in similar companies and the returns of the industry are declining, an investor may still be able to obtain above average risk adjusted returns. One method is by choosing a venture capital manager that has sufficient expertise to beat the average venture capital return through a focused strategy. One problem with this alternative is that managers have an incentive to limit the number of investors in order to reduce the risk of missing a capital call. Investing with a focused fund of sufficient quality may not be possible for an individual investor. An alternative is to choose a venture capital manager that has a strong enough reputation and network to ensure that they are able to invest in the best firms in an attractive industry. Since the amount of equity with a high probability of success is limited and there is an excess supply of capital, it is critical that a venture firm that pursues a diversified strategy be able to invest in the best firms. Without this network and reputation, investors should not expect above average risk adjusted returns.

Cambridge Associates Private Equity and Venture Capital Funds Closed out First Half of 2010 with 5th Consecutive Quarter of Positive Returns November 2010

Ghalbouni, Joseph and Rouziès, Dominique The VC Shakeout. Harvard Business Review, Jul/Aug 2010, Vol. 88, Issue 7/8

Zider, Bob How Venture Capital Works. Harvard Business Review, Nov/Dec 1998

Beating the Market Part 2

In Part 1, I discussed some of the structural reasons why it is easy to beat the market that stem from the incompleteness of markets. The investment policy statement limits most market participants to specific assets and factors such as liquidity, bid-ask spreads, and risk management limit the remaining market participants from maintaining constant market efficiency. If we look at the largest market participants, they are typically long only institutional investors with relatively low portfolio churn rates. A good representative portfolio consists of the following five asset classes: U.S. Equities, International Equities, Fixed Income, REITs, and Alternative Investments.

Using monthly data from January 1990 through December 2009, average correlations among asset classes range from .0942 to .7708 with the highest correlation between U.S. equities and alternatives and the lowest correlation between bonds and alternatives. A moving block bootstrap of this return series shows a optimal tactical band size of around 2% assuming a constant band size across all asset classes and maximizing the information ratio.

From January 2007 through December 2009 correlations ranged from .1304 to .9209 with the highest correlation between international and U.S. equities and the lowest correlation again between bonds and alternatives. Contrast that with the period from January 2001 through December 2003 when correlations ranged from -.3830 to .8978 and the return from asset allocation was far greater. No doubt decreasing the time interval would reveal much larger swings in correlations but few institutions have substantial daily liquidity needs relative to the size of the portfolio so large tactical allocation decisions are not needed.

If you made it this far, you are no doubt wondering why you should care about this. The biggest concern is setting tactical asset allocations for policy statements or how much to deviate from your target allocation. The full 20 year period suggests that periods of higher correlations result in an optimal tactical band size of around 2% to maximize the portfolio information ratio while shorter periods of lower correlations could be used to justify taking more risk. This should be considered when setting the investment policy statement and compensation packages for investment managers if the information ratio is a meaningful measure for you. If total return is the most important metric, larger band size always increases the potential return but has a diminishing effect as correlations increase. This means that the biggest risk takers are not going to receive the benefits of asset diversification when they need it most. In order to produce sufficient returns to justify active management expense, asset selection becomes more important as the economies of scale decrease. In other words, the smaller the portfolio, better asset selection and fewer holdings are needed to justify the expense of active management. Small portfolios cannot rely on index funds and good rebalancing discipline so hiring a portfolio manager that does not engage in asset selection is a waste of money.

Disclaimer: There are clearly many problems with this analysis such as the arbitrary time periods, disclosure of initial allocations, and the simplicity of the rebalancing rule. Unfortunately, I cannot write a complete post at this time without straining any working relationships despite the fact that these results are easily replicated, however; since I am not being compensated for my work, I consider it to be my own.

Beating the Market Part 1

In 1975, John Bogle started the first index mutual fund on the theory that active management does not produce consistently superior returns to justify the added expense. Is this argument consistent with the Efficient Market Hypothesis? I will argue that the idea of “passive management” is misleading due to the practical nature of implementation. There exist only varying degrees of active management with different costs, risks, and expected returns. While it is necessary to question what strategies are worth the cost, the question of active or passive management assumes that there is an identifiable difference between the two choices. If you believe that there is a passive strategy, please leave a detailed description so that I can reply with the active management components that it contains.

The primary problem with measuring performance is that you must first define “the market”. However you define the market, it must be investable directly through an index fund, ETF, or individual assets and this is where the problem occurs. Suppose that you track every company listed on every exchange and you decide to hold a proportional number of shares in each to construct a market capitalized weighted average. In order to maintain the proper weightings, you must constantly buy and sell stocks that are often illiquid. Assuming that the trading fairy solves the liquidity problem, there are still practical limits on what to define as a sufficient change in the market that would justify rebalancing. If we assume that all international equities are part of the market, do currency fluctuations trigger a rebalance? If we segment the market by national boundaries, how do we account for multinational companies or companies where the majority of assets and primary operations are located in another country? In order to answer these questions, indices are created and maintained by various organizations such as Standard and Poor, Dow Jones, or MSCI. The rules are never perfect but if the index is successful, we can infer that industry considers it to be good enough. The rule set can be interpreted as a policy portfolio because it defines what assets will be held, under what conditions assets will be exchanged, and the frequency and methodology of rebalancing. The goal of active management is to simply construct a better rule set than the index.

If you are an asset manager that is benchmarked to one of these indices, you typically have several advantages over the index. The first is the frequency with which you can alter the portfolio enabling you to react faster to new information. The second is the portfolio construction methodology. An index rule set must be predictable which means that the rules that govern whether an asset is included must limit discretion and focus on objective measures such as market capitalization or industry. While a manager may be constrained, they are typically allowed more freedom to choose assets that do not conform to the index inclusion criteria. The manager may also choose assets based on criteria that has been proven to produce superior performance. Few indices are rebalanced based on fundamental business performance. Finally, by underweighting assets, the manger can effectively short an asset relative to the benchmark. This allows the manager to outperform the index regardless of market direction.

Given these advantages, the ability to beat an index using an enhanced index strategy is the result of sufficient work combined with prudent risk controls. The difficulty arises when you determine the amount of outperformance necessary to justify the added expense of active management. While scale certainly matters, the overriding factor is the correlations between individual assets in the case of a single asset class portfolio or the correlations between asset classes in a multi-asset class portfolio. Correlations among asset classes are typically lower than correlations between assets in the same asset class, so the value add of active management is greater for asset class selection than individual asset selection within a diversified portfolio. In a concentrated portfolio, each asset represents a greater portion of asset class selection relative to asset selection. Producing superior returns from a concentrated portfolio requires greater skill in individual asset selection because each asset must outperform its asset class in order to beat a multi-asset class benchmark.

In Part 2 I will discuss some practical examples of active management in a multi-asset class framework using data over the past 20 years, how this relates to the EMH, and what it means to investors.

Risk Aversion and Job Creation

The current consensus view is that the economy will remain weak until consumers start spending again. In the near-term, consumers will increase spending as they become more confident about their job situation and in the long-term, they will need to repair their balance sheets before returning to the level of spending we were accustomed to. This is what many people refer to as “New Normal” since consumers are unlikely to be capable of returning to the old level of spending without another debt bubble. Many people believe that jobs will be created as economic activity increases but this circular argument is being refuted as the largest public companies accumulate cash and continue to grow profits. This is where politics complicates the narrative as analysts attribute the lack of job creation to uncertainty about taxes, healthcare or some other partisan issue. The bottom line is that we still live in a capitalist system regardless of the marginal tax rate or the method of healthcare care administration and business is only rewarded for successfully taking on risk.

Business risk is largely borne by small business since large company employees are rewarded for maintaining the status quo in order to protect their existing competitive advantage. (When you make buggy whips, it doesn’t matter if you’re a fox or a hedgehog and every business eventually enters the buggy whip stage.) As more of our economic activity is dominated by large companies, there is a decreasing impact from real entrepreneurs that are willing to take risk and we should not be surprised when the risk aversion of the entire economy increases. While tax cuts certainly increase profits, there is no direct connection to wealth creation as opposed to wealth transfer. Jobs will be created when businesses are willing to show some leadership and take on risk. Everything else is just politics.

Behavioral Finance

After one of my finance professors declared that there was insufficient literature on any financial theories that contradict the CAPM, I realized that the academic establishment did not simply ignore behavioral finance; it ignored any theory that did not resemble a mathematical proof. While I do enjoy a particularly clever proof now and then, mathematics does not offer sufficient accuracy for market prediction and I doubt that it ever will. The problem exists in both the tools that we use to tackle the fundamental problems of finance as well as the philosophy that determines the assumptions of those models.

Much of what underlies the belief in efficient markets is based on the Expected Utility Hypothesis formulated by Daniel Bernoulli in 1738. The theory states that the value of an asset is equal to the probability weighted expected values. Using a lottery example, one would expect to pay $1 for a ticket where the payout is $1 million and there are 1 million tickets sold. Expected utility works fine when the probabilities are known but fails to explain why investors are averse to ambiguity.

The Ellsberg paradox describes violations to the expected utility hypothesis due to uncertain probabilities. The existence of ambiguous probabilities separates the market from the casino exposing investors to Knightian uncertainty and results in real world decision making that does not conform to expected utility. The missing ingredients in the expected utility hypothesis are the miscalculations of risk about risk and the psychological costs or benefits associated with extreme events.

In 2002, Daniel Kahneman received the Nobel Prize in Economics for his work with Amos Tversky on behavioral finance. Kahneman and Tversky introduced Prospect Theory in 1979 which was later developed into Cumulative Prospect Theory through the use of rank-dependent weightings in 1992. The big new idea presented in CPT is that people base decisions on a reference point rather than absolute utility and people exhibit risk aversion and risk seeking behavior as the magnitude of the outcome increases. This leads to a utility function that has both concave and convex portions while also exhibiting overall risk aversion since more utility is gained from minimizing losses than maximizing gains. In the concave portion, people exhibit risk aversion and engage in behavior such as buying insurance against a low probability event with a large negative effect. In the convex portion, people exhibit risk seeking behavior such as buying a lottery ticket that has a low probability large payout. The key point of CPT is that the same individual will engage in both behaviors because there is additional utility gained or lost when a large effect from a low probability event occurs. This is why gamblers prefer bets on an unlikely winners than expected winners. For a more quantitative explanation, read up on stochastic dominance. Here’s Daniel Kahneman addressing the Georgetown University class of 2009 via Fora.

One problem I do have with behavioral finance is that there seem to be many theories that explain everything while predicting nothing. This is fine for philosophers in search of Truth but not very good for those of us managing money. Perhaps someone a little more or a lot less intelligent than I might conclude that human behavior is inherently unpredictable, but that is likely what draws most of us to study finance; the promise of predictability is just over the next mountain.

Quant N00bs

Boys go through a dinosaur phase, girls go through a horse phase, and it seems that many engineers and computer programmers go through a quant phase. Having studied some nonlinear dynamics, they believe that with so many numbers, the market is just another system that can be tamed with a few well written equations. Despite my somewhat condescending tone, I do not wish to dissuade any more whiz kids from daytrading. I made good money beating these guys on the buy side in the equity markets and I would have made far more on commissions if I were a broker. The one thing I would ask is that you not keep comparing the market to a casino.

I know you hear the media often referring to “gambling” with other people’s money but having played some on your own, you should now know the difference between Knightian risk and Knightian uncertainty. If you manage to stay solvent long enough, you will likely come to the conclusion that markets exhibit less Knightian risk than Knightian uncertainty despite what you may have read about VaR, Bachelier, GARP, or A Random Walk Down Wall Street. (And no, Taleb was not the first one to think of this though he was the first to make the watered down version accessible to the general public and make far more money than he is worth in speaking fees.)

Does this mean that the distinction between Knightian risk and uncertainty should not exist? Ask any entrepreneur or manager with real operational responsibility and they will tell you how much time they spend trying to develop an organizational structure that handles the usual problems while keeping the organization flexible enough to deal with unknown problems. By assuming that all problems cannot be anticipated, the organization is not prepared to handle any problem whether expected or unexpected and is equivalent to sticking your head in the sand. The same is true in investing. By trading stocks when you don’t understand how the company makes money, you are exposing yourself to the same total risk as the skilled investor as you enter the position but increasing your risk as you stay invested since you have less information than the skilled investor. The access to information comes not from reports,  insider trading, or the latest rumor; it comes from not being able to interpret the information that you already have access to. The longer you stay invested, the more the interpretation of information matters.

Given that last statement you may be wondering if you don’t know anything about a company, why shouldn’t you stick to technical analysis and trade frequently. The reason is simple. Long time horizons are made up of many short time horizons. Like VaR, the fact that there is a 99% chance of not losing greater than x amount of your portfolio in the next 10 days does not mean that the probability of losing more than x amount in the next 10 days is 1% because assumptions on methodology will be proven wrong when you need them most. When the market moves against you, there is not always a forward looking indicator and when there is, it may not have been reliable in the past. Even understanding the fact that this time is not different does not necessarily mean that historic data will help you because history rhymes but doesn’t repeat itself.

Now, before you show me the massive amounts of money you have made, please learn how to compute compound time-weighted returns and include all operating costs such as commissions, leverage, and information access. If you need help, look up GIPS. Understand that telling me you made 800% on your favorite trade does not constitute any meaningful performance information.


Financial markets depend on people’s decisions and are therefore irrational. This is not an indictment of investors’ cognitive abilities but a simple understanding that people are not machines and do not make decisions like them. The inconvenient consequence of this fact is that no set of mechanical rules can ever either predict the future with precision or fully explain the past and present. Fortunately, we can still provide value to our clients without needing to work in absolutes. Not only can we live in a world of constant imperfect knowledge, but we can be successful without being 100% correct. I do not mean to imply that we should discard all quantitative approaches to finance, rather we need to understand all the possible risks associated with a model and be conservative in how we value the information it produces.

Security Analysis


I created this post to introduce students to securities analysis. The information provided on this page is not intended to be comprehensive but is intended to provide students with enough information to start learning on their own. There are many perspectives on investment analysis but this page will be limited to fundamental analysis which is best suited to long time horizons. This approach can also be used to evaluate projects in corporate finance.

An investment recommendation needs a minimum of three components: a specific security, the price of that security, and a specific time. The first two are self explanatory but the third is a proxy for a particular information set. This means that buying a particular security at a particular price may or may not be a good idea depending on what we know about the state of the world at any given time. (I will discuss this more under the Process section.)

There are two basic approaches to valuation: relative value and intrinsic value. Relative value utilizes market multiples as a common basis of comparison between two firms. The most common market multiples are: price to earnings, price to book, price to sales, and price to cash flow. Certain market multiples may be useful in some industries and not others. Remember to account for differences in fiscal year end when comparing multiples. Four of the most commonly used time frames for the market multiple denominators are: last fiscal year, trailing twelve months (TTM), next twelve months (NTM), and next fiscal year. Each of these time frames has positives and negatives that vary from industry to industry. Intrinsic valuation is most commonly done using discounted cash flows methodologies such as the dividend discount model, free cash flow to the firm, or free cash flow to equity.


Securities analysis is as much art as science. In addition to making reasonable assumptions about financial data, the analyst must consider many qualitative aspects of the company and the market.

Identify the information set that will determine asset prices in the future.
What factors matter to the firm’s business model? Which of these factors will matter to the firm years from now?
Is this different from the information set that is currently determining prices?
Are other investors basing their valuations off of short term factors such as next quarter’s results, an investment theme, technical analysis, etc…
Is the relevant information set accurate?
Are the assumptions being used to value the firm reasonable? If they differ from your assumptions, is it because you are missing information or is there a disagreement on the interpretation of available information?

Research Questions

These are some of the questions you should ask yourself as you learn about a company. Again, this list is not intended to be comprehensive; only thought provoking.

Who are the firm’s main competitors?
How do market multiples compare with the firm’s competitors?
What are the performance limits to the firm’s business model? What are peak margins and why?
How flexible is the business model? Do high fixed costs prevent it from quickly reacting to changes in demand?
What portion of the firm’s valuation is composed of physical assets and what portion is composed of intangible assets?
Is the market’s valuation of intangible assets reasonable?
What types of investors are invested in the firm and what types of investors are trading the security?

Risks: operational execution, financial execution, macroeconomic, legal, regulatory, headline risk
Management: quality, reliability, accounting standards, strategy, public relations, ethics

Getting Information

Financial websites have a good presentation of the superficial information about a company. A small sample would include Bloomberg, Google, MarketWatch, Reuters, Seeking Alpha, and Yahoo. Transcripts of earnings calls may be obtained from the company investor relations website or from other investor websites. There may be some well written blog posts that are useful for background but for a bottom up fundamental analysis of a company, you need to use primary sources.

The Securities and Exchange Commission (SEC) maintains a database of company filings called the EDGAR database. The most relevant form is the annual 10-K statement which includes the three financial statements (income statement, balance sheet, and statement of cash flows) as well as all the background information needed to understand the business. The 10-Q statement is the shorter version released on a quarterly basis. International firms file 6-K forms with similar information. On the top of the search results for a particular company’s filings is a link to the RSS feed. Copy this link and paste it into an RSS reader. Email applications like Outlook and Mac Mail also have RSS reader capabilities. When a company releases new information, the filing will appear similar to an email in your RSS reader. Press releases can be obtained from business news aggregator sites like Business Wire.

For economic information, the Federal Reserve maintains the FRED database. This is a good source for interest rates, GDP, inflation data, international trade, and other macroeconomic variables. The one year constant maturity treasury bill series DGS1 is a good series to use as the risk free rate.

Final Notes

I suggest using a forecast period of five years (current year plus four) for discounted cash flow models. The terminal value should use very conservative assumptions such as a growth rate in line with long term GDP growth rather than historic firm growth. In addition to learning Excel, I recommend a getting a financial calculator such as the BA II Plus from Texas Instruments or the HP-12C. Check the library for additional books, blogs, magazines, and movie recommendations.

Bob Farrell’s Ten Market Rules to Remember

1. Markets tend to return to the mean over time
2. Excesses in one direction will lead to an opposite excess in the other direction
3. There are no new eras, excesses are never permanent
4. Exponentially rapidly rising or falling markets usually go further than you think, but they do not correct by going sideways
5. The public buys the most at the top, the least at the bottom
6. Fear and greed are stronger than long-term resolve
7. Markets are strongest when they are broad, and weakest when they narrow to a handful of blue-chip names
8. Bear markets have three stages: sharp down, reflexive rebound and a draw-out fundamental downtrend
9. When all experts and forecasts agree, something else is going to happen
10. Bull markets are more fun than bear markets

Career Strategy for the Sell Side

I read a research report this morning from JP Morgan analyst Ehud Gelblum regarding the Qualcomm settlement with Nokia. The report was dated 25 July 2008 but used the stock price from 18 July 2008 to justify the Buy rating. If I knew that the stock was going to pop 15% then I would have bought more of it last week too. Usually, I would not be surprised given my complete lack of respect for sell side analysts but I met Dr. Gelblum at the JP Morgan Tech conference earlier this year and left with the impression of a sharp guy who actually understands technology rather than the usual cream puffs that JP Morgan calls financial analysts. I’m sure there is some jealousy involved in this post. As a buy side analyst I am evaluated on actual results and it would certainly be easier to get paid twice as much to tell people to buy stocks last week. If you want to be a historian, stop calling yourself an analyst. It’s embarrassing to the rest of us.